OpenRaiser/Intern-Atlas huggingface dataset:
Inducing Generalized Multi-Label Rules with Learning Classifier Systems
Venue: arXiv cs.NE 2015
Year: 2015

In recent years, multi-label classification has attracted a significant body of research, motivated by real-life applications, such as text classification and medical diagnoses. Although sparsely studied in this context, Learning Classifier Systems are naturally well-suited to multi-label classification problems, whose search space typically involves multiple highly specific niches. This is the motivation behind our current work that introduces a generalized multi-label rule format -- allowing for flexible label-dependency modeling, with no need for explicit knowledge of which correlations to search for -- and uses it as a guide for further adapting the general Michigan-style supervised Learning Classifier System framework. The integration of the aforementioned rule format and framework adaptations results in a novel algorithm for multi-label classification whose behavior is studied through a set of properly defined artificial problems. The proposed algorithm is also thoroughly evaluated on a set of multi-label datasets and found competitive to other state-of-the-art multi-label classification methods.
An Analytical Evaluation of Matricizing Least-Square-Errors Curve Fitting to Support High Performance Computation on Large Datasets
Venue: arXiv cs.DC 2015
Year: 2015

The procedure of Least Square-Errors curve fitting is extensively used in many computer applications for fitting a polynomial curve of a given degree to approximate a set of data. Although various methodologies exist to carry out curve fitting on data, most of them have shortcomings with respect to efficiency especially where huge datasets are involved. This paper proposes and analyzes a matricized approach to the Least Square-Errors curve fitting with the primary objective of parallelizing the whole algorithm so that high performance efficiency can be achieved when algorithmic execution takes place on colossal datasets.
Using Data Analytics to Detect Anomalous States in Vehicles
Venue: arXiv cs.AI 2015
Year: 2015

Vehicles are becoming more and more connected, this opens up a larger attack surface which not only affects the passengers inside vehicles, but also people around them. These vulnerabilities exist because modern systems are built on the comparatively less secure and old CAN bus framework which lacks even basic authentication. Since a new protocol can only help future vehicles and not older vehicles, our approach tries to solve the issue as a data analytics problem and use machine learning techniques to secure cars. We develop a Hidden Markov Model to detect anomalous states from real data collected from vehicles. Using this model, while a vehicle is in operation, we are able to detect and issue alerts. Our model could be integrated as a plug-n-play device in all new and old cars.
Large deviation, Basic Information Theory for Wireless Sensor Networks
Venue: arXiv cs.IT 2015
Year: 2015

In this article, we prove Shannon-MacMillan-Breiman Theorem for Wireless Sensor Networks modelled as coloured geometric random graphs. For large $n,$ we show that a Wireless Sensor Network consisting of $n$ sensors in $[0,1]^d$ connected by an average number of links of order $n\log n $ can be coded by about $[n(\log n )^2π^{d/2}/(d/2)!]\,\mathcal{H}$ bits, where $\mathcal{H}$ is an explicitly defined entropy. In the process, we derive a joint large deviation principle (LDP) for the \emph{empirical sensor measure} and \emph{the empirical link measure} of coloured random geometric graph models.
A Multiresolution Clinical Decision Support System Based on Fractal Model Design for Classification of Histological Brain Tumours
Venue: arXiv cs.CV 2015
Year: 2015

Tissue texture is known to exhibit a heterogeneous or non-stationary nature, therefore using a single resolution approach for optimum classification might not suffice. A clinical decision support system that exploits the subband textural fractal characteristics for best bases selection of meningioma brain histopathological image classification is proposed. Each subband is analysed using its fractal dimension instead of energy, which has the advantage of being less sensitive to image intensity and abrupt changes in tissue texture. The most significant subband that best identifies texture discontinuities will be chosen for further decomposition, and its fractal characteristics would represent the optimal feature vector for classification. The performance was tested using the support vector machine (SVM), Bayesian and k-nearest neighbour (kNN) classifiers and a leave-one-patient-out method was employed for validation. Our method outperformed the classical energy based selection approaches, achieving for SVM, Bayesian and kNN classifiers an overall classification accuracy of 94.12%, 92.50% and 79.70%, as compared to 86.31%, 83.19% and 51.63% for the co-occurrence matrix, and 76.01%, 73.50% and 50.69% for the energy texture signatures, respectively. These results indicate the potential usefulness as a decision support system that could complement radiologists diagnostic capability to discriminate higher order statistical textural information, for which it would be otherwise difficult via ordinary human vision.
Model Checking Implantable Cardioverter Defibrillators
Venue: arXiv cs.SY 2015
Year: 2015

Ventricular Fibrillation is a disorganized electrical excitation of the heart that results in inadequate blood flow to the body. It usually ends in death within seconds. The most common way to treat the symptoms of fibrillation is to implant a medical device, known as an Implantable Cardioverter Defibrillator (ICD), in the patient's body. Model-based verification can supply rigorous proofs of safety and efficacy. In this paper, we build a hybrid system model of the human heart+ICD closed loop, and show it to be a STORMED system, a class of o-minimal hybrid systems that admit finite bisimulations. In general, it may not be possible to compute the bisimulation. We show that approximate reachability can yield a finite simulation for STORMED systems, which improves on the existing verification procedure. In the process, we show that certain compositions respect the STORMED property. Thus it is possible to model check important formal properties of ICDs in a closed loop with the heart, such as delayed therapy, missed therapy, or inappropriately administered therapy. The results of this paper are theoretical and motivate the creation of concrete model checking procedures for STORMED systems.
Part-Stacked CNN for Fine-Grained Visual Categorization
Venue: arXiv cs.CV 2015
Year: 2015

In the context of fine-grained visual categorization, the ability to interpret models as human-understandable visual manuals is sometimes as important as achieving high classification accuracy. In this paper, we propose a novel Part-Stacked CNN architecture that explicitly explains the fine-grained recognition process by modeling subtle differences from object parts. Based on manually-labeled strong part annotations, the proposed architecture consists of a fully convolutional network to locate multiple object parts and a two-stream classification network that en- codes object-level and part-level cues simultaneously. By adopting a set of sharing strategies between the computation of multiple object parts, the proposed architecture is very efficient running at 20 frames/sec during inference. Experimental results on the CUB-200-2011 dataset reveal the effectiveness of the proposed architecture, from both the perspective of classification accuracy and model interpretability.
Towards Distributed Convoy Pattern Mining
Venue: arXiv cs.DC 2015
Year: 2015

Mining movement data to reveal interesting behavioral patterns has gained attention in recent years. One such pattern is the convoy pattern which consists of at least m objects moving together for at least k consecutive time instants where m and k are user-defined parameters. Existing algorithms for detecting convoy patterns, however do not scale to real-life dataset sizes. Therefore a distributed algorithm for convoy mining is inevitable. In this paper, we discuss the problem of convoy mining and analyze different data partitioning strategies to pave the way for a generic distributed convoy pattern mining algorithm.
Self-Excitation: An Enabler for Online Thermal Estimation and Model Predictive Control of Buildings
Venue: arXiv cs.SY 2015
Year: 2015

This paper investigates a method to improve buildings' thermal predictive control performance via online identification and excitation (active learning process) that minimally disrupts normal operations. In previous studies we have demonstrated scalable methods to acquire multi-zone thermal models of passive buildings using a gray-box approach that leverages building topology and measurement data. Here we extend the method to multi-zone actively controlled buildings and examine how to improve the thermal model estimation by using the controller to excite unknown portions of the building's dynamics. Comparing against a baseline thermostat controller, we demonstrate the utility of both the initially acquired and improved thermal models within a Model Predictive Control (MPC) framework, which anticipates weather uncertainty and time-varying temperature set-points. A simulation study demonstrates self-excitation improves model estimation, which corresponds to improved MPC energy savings and occupant comfort. By coupling building topology, estimation, and control routines into a single online framework, we have demonstrated the potential for low-cost scalable methods to actively learn and control buildings to ensure occupant comfort and minimize energy usage, all while using the existing building's HVAC sensors and hardware.
New Perspectives on $k$-Support and Cluster Norms
Venue: arXiv cs.LG 2015
Year: 2015

We study a regularizer which is defined as a parameterized infimum of quadratics, and which we call the box-norm. We show that the k-support norm, a regularizer proposed by [Argyriou et al, 2012] for sparse vector prediction problems, belongs to this family, and the box-norm can be generated as a perturbation of the former. We derive an improved algorithm to compute the proximity operator of the squared box-norm, and we provide a method to compute the norm. We extend the norms to matrices, introducing the spectral k-support norm and spectral box-norm. We note that the spectral box-norm is essentially equivalent to the cluster norm, a multitask learning regularizer introduced by [Jacob et al. 2009a], and which in turn can be interpreted as a perturbation of the spectral k-support norm. Centering the norm is important for multitask learning and we also provide a method to use centered versions of the norms as regularizers. Numerical experiments indicate that the spectral k-support and box-norms and their centered variants provide state of the art performance in matrix completion and multitask learning problems respectively.
Incentivizing Exploration with Heterogeneous Value of Money
Venue: arXiv cs.GT 2015
Year: 2015

Recently, Frazier et al. proposed a natural model for crowdsourced exploration of different a priori unknown options: a principal is interested in the long-term welfare of a population of agents who arrive one by one in a multi-armed bandit setting. However, each agent is myopic, so in order to incentivize him to explore options with better long-term prospects, the principal must offer the agent money. Frazier et al. showed that a simple class of policies called time-expanded are optimal in the worst case, and characterized their budget-reward tradeoff. The previous work assumed that all agents are equally and uniformly susceptible to financial incentives. In reality, agents may have different utility for money. We therefore extend the model of Frazier et al. to allow agents that have heterogeneous and non-linear utilities for money. The principal is informed of the agent's tradeoff via a signal that could be more or less informative. Our main result is to show that a convex program can be used to derive a signal-dependent time-expanded policy which achieves the best possible Lagrangian reward in the worst case. The worst-case guarantee is matched by so-called "Diamonds in the Rough" instances; the proof that the guarantees match is based on showing that two different convex programs have the same optimal solution for these specific instances. These results also extend to the budgeted case as in Frazier et al. We also show that the optimal policy is monotone with respect to information, i.e., the approximation ratio of the optimal policy improves as the signals become more informative.
Structured Pruning of Deep Convolutional Neural Networks
Venue: arXiv cs.NE 2015
Year: 2015

Real time application of deep learning algorithms is often hindered by high computational complexity and frequent memory accesses. Network pruning is a promising technique to solve this problem. However, pruning usually results in irregular network connections that not only demand extra representation efforts but also do not fit well on parallel computation. We introduce structured sparsity at various scales for convolutional neural networks, which are channel wise, kernel wise and intra kernel strided sparsity. This structured sparsity is very advantageous for direct computational resource savings on embedded computers, parallel computing environments and hardware based systems. To decide the importance of network connections and paths, the proposed method uses a particle filtering approach. The importance weight of each particle is assigned by computing the misclassification rate with corresponding connectivity pattern. The pruned network is re-trained to compensate for the losses due to pruning. While implementing convolutions as matrix products, we particularly show that intra kernel strided sparsity with a simple constraint can significantly reduce the size of kernel and feature map matrices. The pruned network is finally fixed point optimized with reduced word length precision. This results in significant reduction in the total storage size providing advantages for on-chip memory based implementations of deep neural networks.
Block Advertisement Protocol
Venue: arXiv cs.CR 2015
Year: 2015

Bitcoin, a decentralized cryptocurrency, has attracted a lot of attention from academia, financial service industry and enthusiasts. The trade-off between transaction confirmation throughput and centralization of hash power do not allow Bitcoin to perform at the same level as modern payment systems. Block Advertisement Protocol is proposed as a step to resolve this issue. The protocol allows block mining and block relaying to happen in parallel. The protocol dictates a miner to advertise the block it is going to mine allowing other miners to collect all the transactions in advance. When a block in mined, only header is relayed since most of data about the block is already known to all miners.
A Rigorous Analysis of AODV and its Variants
Venue: arXiv cs.NI 2015
Year: 2015

In this paper we present a rigorous analysis of the Ad hoc On-Demand Distance Vector (AODV) routing protocol using a formal specification in AWN (Algebra for Wireless Networks), a process algebra which has been specifically tailored for the modelling of Mobile Ad Hoc Networks and Wireless Mesh Network protocols. Our formalisation models the exact details of the core functionality of AODV, such as route discovery, route maintenance and error handling. We demonstrate how AWN can be used to reason about critical protocol correctness properties by providing a detailed proof of loop freedom. In contrast to evaluations using simulation or other formal methods such as model checking, our proof is generic and holds for any possible network scenario in terms of network topology, node mobility, traffic pattern, etc. A key contribution of this paper is the demonstration of how the reasoning and proofs can relatively easily be adapted to protocol variants.
Reconfigurable State Machine Replication from Non-Reconfigurable Building Blocks
Venue: arXiv cs.DC 2015
Year: 2015

Reconfigurable state machine replication is an important enabler of elasticity for replicated cloud services, which must be able to dynamically adjust their size as a function of changing load and resource availability. We introduce a new generic framework to allow the reconfigurable state machine implementation to be derived from a collection of arbitrary non-reconfigurable state machines. Our reduction framework follows the black box approach, and does not make any assumptions with respect to its execution environment apart from reliable channels. It allows higher-level services to leverage speculative command execution to ensure uninterrupted progress during the reconfiguration periods as well as in situations where failures prevent the reconfiguration agreement from being reached in a timely fashion. We apply our framework to obtain a reconfigurable speculative state machine from the non-reconfigurable Paxos implementation, and analyze its performance on a realistic distributed testbed. Our results show that our framework incurs negligible overheads in the absence of reconfiguration, and allows steady throughput to be maintained throughout the reconfiguration periods.
Repositories with Direct Representation
Venue: arXiv cs.DL 2015
Year: 2015

A new generation of digital repositories could be based on direct representation of the contents with rich semantics and models rather than be collections of documents. The contents of such repositories would be highly structured which should help users to focus on meaningful relationships of the contents. These repositories would implement earlier proposals for model-oriented information organization by extending current work on ontologies to cover state changes, instances, and scenarios. They could also apply other approaches such as object-oriented design and frame semantics. In addition to semantics, the representation needs to allow for discourse and repository knowledge-support services and policies. For instance, the knowledge base would need to be systematically updated as new findings and theories reshape it.
Dynamic lens and monovision 3D displays to improve viewer comfort
Venue: arXiv cs.HC 2015
Year: 2015

Stereoscopic 3D (S3D) displays provide an additional sense of depth compared to non-stereoscopic displays by sending slightly different images to the two eyes. But conventional S3D displays do not reproduce all natural depth cues. In particular, focus cues are incorrect causing mismatches between accommodation and vergence: The eyes must accommodate to the display screen to create sharp retinal images even when binocular disparity drives the eyes to converge to other distances. This mismatch causes visual discomfort and reduces visual performance. We propose and assess two new techniques that are designed to reduce the vergence-accommodation conflict and thereby decrease discomfort and increase visual performance. These techniques are much simpler to implement than previous conflict-reducing techniques.
Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies
Venue: arXiv cs.LG 2015
Year: 2015

We consider effort allocation in crowdsourcing, where we wish to assign labeling tasks to imperfect homogeneous crowd workers to maximize overall accuracy in a continuous-time Bayesian setting, subject to budget and time constraints. The Bayes-optimal policy for this problem is the solution to a partially observable Markov decision process, but the curse of dimensionality renders the computation infeasible. Based on the Lagrangian Relaxation technique in Adelman & Mersereau (2008), we provide a computationally tractable instance-specific upper bound on the value of this Bayes-optimal policy, which can in turn be used to bound the optimality gap of any other sub-optimal policy. In an approach similar in spirit to the Whittle index for restless multiarmed bandits, we provide an index policy for effort allocation in crowdsourcing and demonstrate numerically that it outperforms other stateof- arts and performs close to optimal solution.
Evolving Non-linear Stacking Ensembles for Prediction of Go Player Attributes
Venue: arXiv cs.AI 2015
Year: 2015

The paper presents an application of non-linear stacking ensembles for prediction of Go player attributes. An evolutionary algorithm is used to form a diverse ensemble of base learners, which are then aggregated by a stacking ensemble. This methodology allows for an efficient prediction of different attributes of Go players from sets of their games. These attributes can be fairly general, in this work, we used the strength and style of the players.
Counting independent terms in big-oh notation
Venue: arXiv cs.CC 2015
Year: 2015

The field of computational complexity is concerned both with the intrinsic hardness of computational problems and with the efficiency of algorithms to solve them. Given such a problem, normally one designs an algorithm to solve it and sets about establishing bounds on its performance as functions of the algorithm's variables, particularly upper bounds expressed via the big-oh notation. But if we were given some inscrutable code and were asked to figure out its big-oh profile from performance data on a given set of inputs, how hard would we have to grapple with the various possibilities before zooming in on a reasonably small set of candidates? Here we show that, even if we restricted our search to upper bounds given by polynomials, the number of possibilities could be arbitrarily large for two or more variables. This is unexpected, given the available body of examples on algorithmic efficiency, and serves to illustrate the many facets of the big-oh notation, as well as its counter-intuitive twists.
Dynamic Cluster Head Selection Using Fuzzy Logic on Cloud in Wireless Sensor Networks
Venue: arXiv cs.NI 2015
Year: 2015

One of the most vital activities to reduce energy consumption in wireless sensor networks is clustering. In clustering, one node from a group of nodes is selected to be a cluster head, which handles majority of the computation and processing for the nodes in the cluster. This paper proposes an algorithm for fuzzy based dynamic cluster head selection on cloud in wireless sensor networks. The proposed algorithm calculates a Potential value for each node and selects cluster heads with high potential. The proposed algorithm minimizes cluster overlapping by spatial distribution of cluster heads and discards malicious nodes i.e. never allows malicious nodes to be cluster heads.
Adaptive type-2 fuzzy second order sliding mode control for nonlinear uncertain chaotic system
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, a robust adaptive type-2 fuzzy higher order sliding mode controller is designed to stabilize the unstable periodic orbits of uncertain perturbed chaotic system with internal parameter uncertainties and external disturbances. In Higher Order Sliding Mode Control (HOSMC),the chattering phenomena of the control effort is reduced, by using Super Twisting algorithm. Adaptive interval type-2 fuzzy systems are proposed to approximate the unknown part of uncertain chaotic system and to generate the Super Twisting signals. Based on Lyapunov criterion, adaptation laws are derived and the closed loop system stability is guaranteed. An illustrative example is given to demonstrate the effectiveness of the proposed controller.